Welcome to Oscars-ology, in which we anthropologically and scientifically (and sometimes mathematically) dissect some facet of the Academy Awards for the obsessed. Today: how statisticians predict the winners, and how “swarm creativity” (read: crazy commenters) feed into the mix.

How do you know when you’re totally, hopelessly, pathologically consumed by the Oscars? When you not only get up at four a.m. and throw a breakfast tea party for the nomination announcements but also seriously consider forging a ticket for the Editors’ Guild Awards? Perhaps Oscar blogs like Awards Daily, In Contention, and Gold Derby no longer sate your lust, so you constantly check online to see what the Bangladesh Financial Express has to say? Or maybe you just track a year’s worth of Oscar conversation on IMDB, use regression analysis on its word counts, and plug that into a custom-designed index to predict Oscar nominees and winners.

Not such a far-fetched thought: in 2007, Dr. Peter Gloor and his colleagues did exactly that, deriving a formula to predict Oscar nominees that actually kind of worked. (They absolutely nailed The Departed as both a nominee and eventual best-picture winner.) Here it is, if you’re (way too) curious:

Oscar model = a∂ + bγ+c*λ + ε|a + b + c = 1

Gloor, a research scientist at the Gloor, a research scientist at the Center for Collective Intelligence at MIT’s Sloan School of Management, has mined the Oscars as a field of analysis for his preferred area of interest: swarm creativity. (Cocktail-party-ese translation: the wisdom of crowds.) Dr. Gloor studies why resources like Wikipedia are remarkably accurate and self-correcting, considering that almost anyone can jump in and post false, mistaken, or just useless information. In fact, crowds, as we all know by now, can often best the experts—which is why someone like Nate Silver, using regression analysis, made countless election pundits predicting a Romney rout look like loud-mouthed kindergartners.

“The experts are saying everything in black and white, and saying everything to increase the attractiveness to be quoted in the news media,” Gloor told me over the phone. “They don’t want to be right—they want to be read.”

O.K., that’s not news, but seriously, I asked, why the Oscars? Ironically, doing Oscar analysis turned out to be a great way to calibrate his methodology. You see, if you’ve ever trolled IMDB’s user forums, you’ll run across hordes of very, very opinionated people with absolutely nothing at stake, which it turns out is exactly what makes crowd wisdom function best and makes IMDB ideal for constructing models of the “social networks” he wants to use for regression analysis. As Dr. Gloor explained, “They are trying to predict what the voters are doing, and they have no hidden incentive. They don’t want to get famous, otherwise they would blog. They just care about the movie and the director.” Gloor’s since expanded his research into stock-, gold-, and oil-market analyses, but those are more difficult because when money is an incentive for people, they tend to get a little delusional—the wisdom of crowds easily becomes the madness of crowds, according to Gloor. “Why? Because I want to get rich quick, and I lose every piece of common sense I have because I have dollar signs in my eyes,” he says. In that sense, the Oscars are surprisingly honest and thus great for creating predictive formulas.

Last year, for example, looking at the big-six categories, Gloor and his students ran an informal analysis—this time weighting IMDB with the Hollywood Stock Exchange and Intrade.com—and, he claims, they were able to accurately predict the nominees for best picture, best director, and about 70 percent of the acting categories. (Sadly for you Oscar-ballot junkies, Gloor never expanded his research to best sound mixing.) Gloor said they have the process for best picture and best director down pat, though actors give them trouble because a lot of the chatter on the forums is about analytically useless but absolutely riveting stuff, like whom Jessica Chastain is dating. (Cough—Tom Hiddleston.) As per Gloor: “directors, they are not so active in their love life—they are speculated about if it’s an Oscar, not if it’s a break-up with a boyfriend and girlfriend.” (Sorry, directors.) The comments concerning them, therefore, are more serious and analytically pure.

So what about IMDB forums is the determining variable that gives them such remarkable predictive power about the Academy’s choices? Gloor and his colleagues look at the most important posters, who exhibit a high degree of “between-ness,” the über-scientific term for individuals in a social network who post the most and are responded to most often and most quickly. If that’s confusing, basically think of it like Google’s page rank—Gloor and his colleagues indeed used the same method as Google does for search. Then they subjected those posters’ writings to word analysis to see which movies are mentioned most favorably for the Oscars. What’s the secret sauce of Gloor’s formula? Passion. “It’s not just positivity; it’s emotionality. The more emotional people are, the more it predicts success,” he says. Thus, the aggregated IMDB fan boys who argue most passionately about the movies they think will get the Oscar—and who are evangelizing the herd to that effect—actually turn out to be pretty good at reading the Academy’s minds.

Unfortunately, Gloor and his students didn’t run the analysis this year: it takes about four weeks and they’ve got better things to guess, like world oil prices. However, Gloor says if you want an inside edge on the Oscars, Intrade.com’s prediction markets work remarkably well. In the big-six categories over the last three years, Intrade has gotten 16 of 18 right. Intrade’s success comes from the fact that people are actually betting on this, and thus putting their money where their digital mouths are—and no one’s going to place a bet on a movie they think will lose.